What are Encoders or autoencoding models in transformers?

This recipe explains what are Encoders or autoencoding models in transformers.

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Recipe Objective - What are Encoders or autoencoding models in transformers?

By distorting the input tokens in some way and attempting to recreate the original text, encoders or autoencoding models are pre-trained. They are similar to the encoder in the original transformer model in that they have full access to all inputs without the need for a mask. Typically, these models construct a bidirectional representation of the entire sentence. They may be fine-tuned and obtain excellent results on a variety of tasks, including text generation, but sentence classification or token classification is their most natural use. BERT is a good example of such a model.

Explore the BERT Variants - ALBERT vs DistilBERT

Types of Encoders or autoencoding models:

* BERT
* ALBERT
* Funnel Transformer
* RoBERTa
* DistilBERT
* ConvBERT
* XLM
* FlauBERT
* ELECTRA
* Longformer
* XLM-RoBERTa

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